Computational Image Analysis Identifies Histopathological Image Features Associated With Somatic Mutations and Patient Survival in Gastric Adenocarcinoma

作者全名："Cheng, Jun; Liu, Yuting; Huang, Wei; Hong, Wenhui; Wang, Lingling; Zhan, Xiaohui; Han, Zhi; Ni, Dong; Huang, Kun; Zhang, Jie"

作者地址："[Cheng, Jun; Liu, Yuting; Hong, Wenhui; Wang, Lingling; Ni, Dong] Shenzhen Univ, Natl Reg Key Technol Engn Lab Med Ultrasound, Shenzhen, Peoples R China; [Cheng, Jun; Liu, Yuting; Hong, Wenhui; Wang, Lingling; Ni, Dong] Shenzhen Univ, Hlth Sci Ctr, Sch Biomed Engn, Guangdong Key Lab Biomed Measurements & Ultrasoun, Shenzhen, Peoples R China; [Cheng, Jun; Ni, Dong] Shenzhen Univ, Marshall Lab Biomed Engn, Shenzhen, Peoples R China; [Huang, Wei] South China Univ Technol, Sch Med, Guangdong Acad Med Sci, Dept Radiat Oncol,Guangdong Prov Peoples Hosp, Guangzhou, Peoples R China; [Zhan, Xiaohui] Chongqing Med Univ, Sch Basic Med, Chongqin, Peoples R China; [Han, Zhi; Huang, Kun] Indiana Univ, Sch Med, Dept Med, Indianapolis, IN 46202 USA; [Zhang, Jie] Indiana Univ Sch Med, Dept Med & Mol Genet, Indianapolis, IN 46202 USA"

通信作者："Ni, D (corresponding author), Shenzhen Univ, Natl Reg Key Technol Engn Lab Med Ultrasound, Shenzhen, Peoples R China.; Ni, D (corresponding author), Shenzhen Univ, Hlth Sci Ctr, Sch Biomed Engn, Guangdong Key Lab Biomed Measurements & Ultrasoun, Shenzhen, Peoples R China.; Ni, D (corresponding author), Shenzhen Univ, Marshall Lab Biomed Engn, Shenzhen, Peoples R China.; Huang, K (corresponding author), Indiana Univ, Sch Med, Dept Med, Indianapolis, IN 46202 USA.; Zhang, J (corresponding author), Indiana Univ Sch Med, Dept Med & Mol Genet, Indianapolis, IN 46202 USA."

来源：FRONTIERS IN ONCOLOGY

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:000639981400001

JCR分区：Q2

影响因子：4.7

年份：2021

卷号：11

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：computational pathology; gastric adenocarcinoma; gastric cancer; whole-slide image; genotype-phenotype association; prognosis

摘要："Computational analysis of histopathological images can identify sub-visual objective image features that may not be visually distinguishable by human eyes, and hence provides better modeling of disease phenotypes. This study aims to investigate whether specific image features are associated with somatic mutations and patient survival in gastric adenocarcinoma (sample size = 310). An automated image analysis pipeline was developed to extract quantitative morphological features from H&E stained whole-slide images. We found that four frequently somatically mutated genes (TP53, ARID1A, OBSCN, and PIK3CA) were significantly associated with tumor morphological changes. A prognostic model built on the image features significantly stratified patients into low-risk and high-risk groups (log-rank test p-value = 2.6e-4). Multivariable Cox regression showed the model predicted risk index was an additional prognostic factor besides tumor grade and stage. Gene ontology enrichment analysis showed that the genes whose expressions mostly correlated with the contributing features in the prognostic model were enriched on biological processes such as cell cycle and muscle contraction. These results demonstrate that histopathological image features can reflect underlying somatic mutations and identify high-risk patients that may benefit from more precise treatment regimens. Both the image features and pipeline are highly interpretable to enable translational applications."

基金机构："National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61901275]; Shenzhen University startup fund [2019131]; Young Faculty Support Program of SZU Health Science Center [71201000001]; National Key R&D Program of China [2019YFC0118300]; Shenzhen Peacock Plan [KQTD2016053112051497, KQJSCX20180328095606003]; Medical Scientific Research Foundation of Guangdong Province, China [B2018031]; American Cancer Society Institutional Research GrantAmerican Cancer Society; Indiana University Precision Health Initiative"

基金资助正文："This study was supported in part in by National Natural Science Foundation of China (No. 61901275 to JC), Shenzhen University startup fund (No. 2019131 to JC), Young Faculty Support Program of SZU Health Science Center (No. 71201000001 to JC), National Key R&D Program of China (No. 2019YFC0118300 to DN), Shenzhen Peacock Plan (No. KQTD2016053112051497 and KQJSCX20180328095606003 to DN), Medical Scientific Research Foundation of Guangdong Province, China (No. B2018031 to DN), American Cancer Society Institutional Research Grant to Indiana University to JZ, and Indiana University Precision Health Initiative to KH and JZ."